Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study

Hou, Lin; Sun, Ning; Mane, Shrikant; Sayward, Fred; Rajeevan, Nallakkandi; Cheung, Kei Hoi; Cho, Kelly; Pyarajan, Saiju; Aslan, Mihaela; Miller, P.; Harvey, Philip D.; Gaziano, J. Michael; Concato, John; Zhao, Hongyu

doi:10.1002/gepi.22027

Cited by 17 publications

(14 citation statements)

References 42 publications

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“…If the studied event is rare, such as de novo germline mutations, the likelihood to observe false positive (FP) calls is further increased (Gómez‐Romero et al., ; Veltman & Brunner, ). Moreover, rare variant association studies (RVAS) can generate false results if genes are enriched with sequencing or alignment errors, leading to false associations to the studied disease (Hou et al., ; Johnston, Hu, & Cutler, ; Yan et al., ). Hence, some RVAS methods take into account error probabilities (He et al., ) or bypass genotype calls completely by directly modeling sequencing reads (Hu, Liao, Johnston, Allen, & Satten, ).…”

Section: Introductionmentioning

confidence: 99%

Allele balance bias identifies systematic genotyping errors and false disease associations

et al. 2018

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In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.

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Section: Introductionmentioning

confidence: 99%

Allele balance bias identifies systematic genotyping errors and false disease associations

et al. 2018

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“…The optimal threshold probability ( π opt ), defined as the π min that maximized the statistical power to detect association between single nucleotide polymorphisms (SNPs) and disease (PTSD), was obtained as follows. For a given π min , the power was computed from the predicted number of PTSD cases and controls obtained with Equation and the uncertainties in phenotyping, assuming multiplicative genetic effects and PTSD prevalence of 0.3 (Hou et al, ). The minor allele frequency (MAF) was varied from 0.10 to 0.50 and relative risk (RR) for disease‐associated SNPs from 1.05 to 1.20.…”

Section: Methodsmentioning

confidence: 99%

Genomics of posttraumatic stress disorder in veterans: Methods and rationale for Veterans Affairs Cooperative Study #575B

Radhakrishnan

Aslan

Harrington

et al. 2019

Int J Methods Psych Res

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Objectives Heritability in the risk for developing posttraumatic stress disorder (PTSD) has been established, but most genome‐wide association studies (GWASs) of PTSD involve relatively small sample sizes and limited identification of associated genetic loci. This report describes the methodology of a Veterans Affairs (VA) Cooperative Studies Program GWAS of PTSD among combat‐exposed U.S. veterans. Methods Probable cases (with PTSD) and probable controls (without PTSD) were identified from among veterans enrolled in the VA Million Veteran Program (MVP) with an algorithm developed using questionnaire responses and electronic health record information. This algorithm, based on a statistical model, relied on medical chart reviews as a reference standard and was refined using telephone interviews. Subsequently, to evaluate the impact of probabilistic phenotyping on statistical power, the threshold probability for case–control selection was varied in simulations. Results As of September 2018, >695,000 veterans have enrolled in MVP. For current analyses, genotyping data were available for >353,000 participants, including >83,000 combat‐exposed veterans. A threshold probability of 0.7 for case and control designation yielded an interim >16,000 cases and >33,000 controls. Conclusions A formal methodological approach was used to identify cases and controls for subsequent GWAS analyses to identify genetic risk loci for PTSD.

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“…results from the analyses performed at the lab). Genotyping methods are constantly improving and the error rate is generally low, given that sample quality is not compromised [83,84]. The latter may well be the case if the application is massive genotyping of biological traces.…”

Section: Tablementioning

confidence: 99%

Forensic genealogy—A comparison of methods to infer distant relationships based on dense SNP data

Kling

Tillmar

2019

Forensic Science International: Genetics

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The concept forensic genealogy was discussed already in 2005 but has recently emerged in relation to the use of public genealogy databases to find relatives of the donor of a crime stain. In this study we explored the results and evaluation of searches conducted in such databases. In particular, we focused on the statistical classification that entails from the search and study the variation observed for different relationship classes. The forensic guidelines advocate the use of the likelihood ratio (LR) as a mean to measure the weight of evidence, which requires exact formulation of competing hypotheses. We contrast the LR approach with alternative approaches relying on identical by state (IBS) measures to estimate the total length of shared genomic segments as well as identical by descent (IBD) coefficients for a pair of individuals.We used freely accessible data from the 1000 Genome project to perform extensive simulations, generating data for a number of distinct relationships. Specifically we studied some overarching relationship classes and the performance of the above-mentioned evaluative approaches to classify a known pair of relatives into each class.The results indicate that the traditional LR approach as a single source of classification is as good as, and in some cases even better than, the alternative approaches. In particular the true classification rate is higher for some distant relationship. However, the LR approach is both computer-intensive and sensitive to population frequencies as well as genetic maps (positions of the markers). We further showed that when combining different classification approaches, a lower false classification rate was achieved while still maintaining a high true classification rate.

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Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study

Cited by 17 publications

References 42 publications

Allele balance bias identifies systematic genotyping errors and false disease associations

Allele balance bias identifies systematic genotyping errors and false disease associations

Genomics of posttraumatic stress disorder in veterans: Methods and rationale for Veterans Affairs Cooperative Study #575B

Forensic genealogy—A comparison of methods to infer distant relationships based on dense SNP data

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